Retrieval-augmented generation

Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.

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arXiv Computer Vision

Sep 30

Minkowski Attractor Networks: Closed-Form Hyperbolic Flows for Visual Representations

arXiv:2609.37817v1 Announce Type: new Abstract: Geometric representation learning predominantly scaffolds representations onto flat Euclidean subspaces or compact product tori ($\mathbb{T}^K$). Howev...

By Zhongping Ji

rag efficiency

arXiv AI

Sep 30

Better Nearest Neighbor Graph Indices via (Efficient) LLM-Guided Pruning

arXiv:2609.36359v1 Announce Type: new Abstract: Graph-based approximate nearest neighbor search (ANNS) is widely used for large-scale semantic search. Its indices are constructed primarily based on g...

By Fangzhou Wu, Haike Xu, Sandeep Silwal

llms rag efficiency benchmarks

arXiv AI

Sep 30

ARCagent: An Adaptive Retrieval Calibration Agent for Clinical Question Answering

arXiv:2609.36392v1 Announce Type: new Abstract: In diseases where clinical guidelines are incomplete, contested, or mutually contradictory, knowledge completeness and dynamic conflict-aware synthesis...

By Yuyan Chen

llms rag agents nlp benchmarks safety

arXiv AI

Sep 30

Proofs Without Nominals: G\"odel's Ontological Argument, its Shallow Embedding, and the Open Questions of the Monatshefte Notes

arXiv:2609.36279v1 Announce Type: cross Abstract: The shallow embedding of higher-order modal logic in classical higher-order logic, used in Benzm\"uller and Scott's Notes on G\"odel's and Scott's va...

By Christoph Benzm\"uller

rag

arXiv AI

Sep 30

OntoTKGE: Ontology-Enhanced Temporal Knowledge Graph Extrapolation

arXiv:2604.05468v3 Announce Type: replace Abstract: Temporal knowledge graph (TKG) extrapolation is an important task that aims to predict future facts through historical interaction information with...

By Dongying Lin, Yinan Liu, Shengwei tang, Bin Wang, Xiaochun Yang

rag efficiency benchmarks

arXiv AI

Sep 30

ATLAS: Aligned Transport of Latent Structure for Reliable World Model Planning

The paper introduces ATLAS, a training objective that preserves relational geometry in latent world models while calibrating the global latent distribution. By transferring normalized pairwise structure from an informative encoder to the planning latent and applying Wasserstein embedding matching, ATLAS improves goal‑reaching success on tasks such as PushT, TwoRoom, and OGBench‑Cube, especially on higher‑novelty episodes. Diagnostics show stronger novelty‑related structure, better marginal calibration, and lower multi‑step prediction error in the planning latent.

By Ke Fang, Yupu Yao, Lu Cheng

rag

arXiv AI

Sep 30

Graph neural networks for sampling-invariant embeddings of organized signal sets

The paper proposes graph neural network encoders that transform organized signal sets—such as ordered radar or sensor signals with varying sampling parameters—into fixed‑size vector representations. These embeddings aim to eliminate sampling differences, enabling topology‑aware processing without traditional signal‑processing constraints. The authors evaluate the discriminative power of the embeddings, focusing on waveform separation, using synthetic complex‑valued radiofrequency signals.

By Martin Bauw (CMM), Santiago Velasco-Forero (CMM), Jesus Angulo (CMA)

rag

arXiv Machine Learning

Sep 30

Dyad: Extending Large Language Models with Native Typed Decision-Making

arXiv:2609.36116v1 Announce Type: new Abstract: We study how to build more capable general-purpose agents by extending large language models (LLMs) with native typed decision-making. We introduce Dya...

By Yundaichuan Zhan, Weishi Wang, Wenbiao Liu, Daniel Dahlmeier, Chengwei Qin, Juncheng Li, Fredrik D. Johansson, Zhongqi Yue

llms rag agents reinforcement-learning safety

arXiv Machine Learning

Sep 30

Action Chunking Proximal Policy Optimization with Feedback Correction

arXiv:2609.36250v1 Announce Type: new Abstract: Action chunking provides temporal abstraction in reinforcement learning by selecting short action sequences instead of individual actions, but many exi...

By Sanghyun Hahn, Jonghyun Choi

rag reinforcement-learning robotics

arXiv Machine Learning

Sep 30

GNA: Granular Neighbor Assembly for Retrieval-Augmented Multivariate Time-Series Forecasting

arXiv:2609.36281v1 Announce Type: new Abstract: Deep forecasters predict from a fixed-length lookback window, and lengthening it gives diminishing returns at a growing cost. Retrieval augmentation in...

By Vincent Uhse

llms rag benchmarks

arXiv Machine Learning

Sep 30

Cheap and Powerful Tests for Supervised Subspaces: Per-Component Inference for PLS

arXiv:2609.36307v1 Announce Type: new Abstract: Partial Least Squares (PLS) regression extracts a few outcome-aligned directions in a high-dimensional X and is widely used across applied science, but...

By Pawe{\l} Lenartowicz, Hubert Plisiecki

rag

arXiv Machine Learning

Sep 30

Harnessing Large Language Models to Compile Task-Relevant Context into Bayesian Optimisation

arXiv:2609.36788v1 Announce Type: new Abstract: Incorporating rich task-relevant context, such as domain knowledge and external observations, is a key capability yet remains challenging for Bayesian...

By Zhongwei Yu, Sourabh Roy, Bin Cao, Xue Yan, Anjie Liu, Jun Wang

llms rag benchmarks

arXiv Machine Learning

Sep 30

Seeing Time: Visual-Temporal Representation Learning for Interpretable Time Series Clustering

arXiv:2609.36873v1 Announce Type: new Abstract: Multivariate Time Series (MTS) clustering is an important tool in temporal data mining, aiming to discover latent group structures from complex observa...

By Zheng Zhu, Zexi Tan, Yuming Deng, Yiqun Zhang

rag safety

arXiv AI

Sep 30

Hierarchical Compression of Vision-Language Model Benchmarks

arXiv:2609.37515v1 Announce Type: cross Abstract: Thorough evaluation of vision-language models (VLMs) has become prohibitively expensive, as benchmarks span an ever-broader spectrum of capabilities...

By Hyunjong Ok, Seunggu Kang, Jaeho Lee

llms rag efficiency multimodal benchmarks

arXiv Machine Learning

Sep 30

Why Adaptive Optimizers Underestimate Rare Tokens

arXiv:2609.37535v1 Announce Type: new Abstract: In the softmax output layer, a rare token receives a small positive logit gradient on most steps and a much larger negative gradient on the few steps w...

By Sangsidhya Kar

llms rag safety

arXiv Machine Learning

Sep 30

Sieve and Sage: Efficient Distraction Filtering for Reliable RALM Abstention

arXiv:2609.35794v1 Announce Type: cross Abstract: Just as Socrates recognized the limits of his own knowledge, Retrieval-Augmented Language Models (RALMs) should learn to abstain when the retrieved e...

By Jongbin Won, Sung Geun An, Jay-yoon Lee

llms rag safety

arXiv AI

Sep 30

Same Bytes, Different Authority: Reserved-Token Representations in Chat-Template Prompt Injection

arXiv:2609.35932v1 Announce Type: cross Abstract: Prompt injection against LLM agents becomes much stronger when the injected instruction is wrapped in the model's own chat template. A forged templat...

By Yan Zhan, Yunze Song, Mengkai Hou, Wanting Zhang, Shaobo Liu, Zhijun Gao

llms rag agents nlp fine-tuning benchmarks

arXiv Machine Learning

Sep 30

One-Step Next-Latent Prediction Is Not a World Model

arXiv:2609.36227v1 Announce Type: cross Abstract: Next-latent prediction fits a map from the current embedding to the next one. LeNEPA carries this objective to time series, replacing the stop-gradie...

By Shitong Wang, Zhongang Cai, Yuzhou Hong

rag

arXiv AI

Sep 30

Emergent Tonal Structure in Learned Chord Embeddings and Its Relation to Tonal Tension

arXiv:2609.36460v1 Announce Type: cross Abstract: Several tonal pitch spaces and computational models have been proposed to analyze tonal structure in Western tonal music, many of them grounded in pr...

By Maral Ebrahimzadeh, Gilberto Bernardes, Sebastian Stober

rag safety

arXiv AI

Sep 30

Safer Content or Firmer Refusals? A Hybrid Perturbation Defense for Alignment under Harmful Fine-tuning

arXiv:2609.36862v1 Announce Type: cross Abstract: Fine-tuning-as-a-service lets users adapt a safety-aligned language model to their own data, but it also creates a harmful fine-tuning attack surface...

By Muhammad Zeeshan Akram, Mufid Kamel Marican, Anvesh Reddy Yenugu, Ali Zain Kaimkhani, Minghong Fang

llms rag fine-tuning safety

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